POS1110 PATIENT CLUSTERS IDENTIFIED BY MACHINE LEARNING: DATA FROM A POOLED ANALYSIS OF PHASE-3 TRIALS OF SECUKINUMAB IN PSORIATIC ARTHRITIS, ANKYLOSING SPONDYLITIS AND PSORIATIC ARTHRITIS WITH AXIAL MANIFESTATIONS
Authors: X. Baraliakos, E. Pournara, D. D. Gladman, P. J. Mease, S. Jahandideh, L. Coates
Journal: Annals of the Rheumatic Diseases (2023)

<h3>Background</h3> Universally acceptable criteria to define axial psoriatic arthritis (PsA) are lacking. Machine learning (ML) algorithms can detect patterns from large clinical datasets and identify clusters with potential therapeutic or prognostic significance in PsA patients (pts) with axial manifestations.<sup>[1]</sup> <h3>Objectives</h3> To identify pt clusters based on baseline (BL) demographics and clinical indicators to support characterisation of the axial PsA phenotype within the spondyloarthritis (SpA) spectrum. <h3>Methods</h3> Pooled BL demographics and clinical data of secukinumab (SEC)-treated pts from 10 Phase-3 studies: FUTURE 1–5 (PsA), MEASURE 1–4 (ankylosing spondylitis, AS) and MAXIMISE (PsA with axial manifestations) were analysed to identify pt clusters by ML.<sup>[2]</sup> Finite mixture model methodology was applied to the pooled dataset. Longitudinal responses of SEC 300 mg vs 150 mg were explored across the identified clusters and clinical indicators tender joint counts (TJC), swollen joint counts (SJC) and enthesitis. <h3>Results</h3> Eight distinct clusters were identified (n=3907, <b>Figure 1 and Table 1</b>). Pts with PsA with axial manifestations (MAXIMISE) were overrepresented in clusters 6–8. Pts in cluster 6 (mean age 48 yrs; 46% male) were overweight with pronounced psoriasis (PsO), and high articular burden in large joints. Pts in cluster 7 (mean age 47 yrs; 53% male) were less overweight with lower polyarticular joint counts (mean 11 joints) and tenderness focused on the feet, wrists and hands. Pts in cluster 8 were mostly pts with AS (mean age 43 yrs; 64% male), less overweight with oligoarthritis and high prevalence of spinal pain. Pts with PsA (FUTURE) were overrepresented in clusters 1–5. Longitudinal analysis showed significant improvements with SEC 300 mg vs 150 mg in clusters 6 and 8 for TJC, and cluster 7 for SJC. <h3>Conclusion</h3> PsA clusters obtained by ML in the pooled dataset indicate phenotypical heterogeneity of pts with PsA with axial manifestations and overlapping features across the SpA spectrum. <h3>References</h3> [1]<i>Pournara E et al. RMD Open 2021;7:e00184</i> [2]<i>Baraliakos X et al. Arthritis Rheumatol 2022; 74 (suppl 9</i>) <h3>Acknowledgements:</h3> NIL. <h3>Disclosure of Interests</h3> Xenofon Baraliakos Consultant of: Abbvie, Amgen, Chugai, Eli Lilly, Galapagos, Janssen, MSD, Novartis, Pfizer, Roche, Sandoz, UCB, Grant/research support from: Abbvie, Amgen, Chugai, Eli Lilly, Galapagos, Janssen, MSD, Novartis, Pfizer, Roche, Sandoz, UCB, Effie Pournara Shareholder of: Novartis, Employee of: Novartis, Dafna D Gladman Consultant of: Amgen, AbbVie, BMS, Celgene, Eli Lilly, Gilead, Galapagos, Janssen, Novartis, Pfizer and UCB, Grant/research support from: Amgen, AbbVie, Celgene, Eli Lilly, Janssen, Novartis, Pfizer and UCB, Philip J Mease Speakers bureau: AbbVie, Amgen, Lilly, Janssen, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Aclaris, Amgen, Boehringer Ingelheim, BMS, Lilly, Galapagos, Gilead, GlaxoSmithKline, Inmagene, Janssen, Novartis, Pfizer, Sun Pharma, and UCB, Grant/research support from: AbbVie, Amgen, BMS, Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, Sun Pharma and UCB, Samad Jahandideh Employee of: Novartis, Laura Coates Speakers bureau: AbbVie, Amgen, Biogen, Celgene, Eli Lilly, Galapagos, Gilead, GSK, Janssen, Medac, Novartis, Pfizer and UCB, Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Gilead, Galapagos, Janssen, Moonlake, Novartis, Pfizer and UCB, Grant/research support from: AbbVie, Amgen, Celgene, Eli Lilly, Janssen, Novartis, Pfizer and UCB.